447 research outputs found
RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation
Retrieving documents and prepending them in-context at inference time
improves performance of language model (LMs) on a wide range of tasks. However,
these documents, often spanning hundreds of words, make inference substantially
more expensive. We propose compressing the retrieved documents into textual
summaries prior to in-context integration. This not only reduces the
computational costs but also relieves the burden of LMs to identify relevant
information in long retrieved documents. We present two compressors -- an
extractive compressor which selects useful sentences from retrieved documents
and an abstractive compressor which generates summaries by synthesizing
information from multiple documents. Both compressors are trained to improve
LMs' performance on end tasks when the generated summaries are prepended to the
LMs' input, while keeping the summary concise.If the retrieved documents are
irrelevant to the input or offer no additional information to LM, our
compressor can return an empty string, implementing selective augmentation.We
evaluate our approach on language modeling task and open domain question
answering task. We achieve a compression rate of as low as 6% with minimal loss
in performance for both tasks, significantly outperforming the off-the-shelf
summarization models. We show that our compressors trained for one LM can
transfer to other LMs on the language modeling task and provide summaries
largely faithful to the retrieved documents
CALD : surviving various application-layer DDoS attacks that mimic flash crowd
Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when suchattacks mimic or occur during the flash crowd event of a popular Website. In this paper, we present the design and implementation of CALD, an architectural extension to protect Web servers against various DDoS attacks that masquerade as flash crowds. CALD provides real-time detection using mess tests but is different from other systems that use resembling methods. First, CALD uses a front-end sensor to monitor thetraffic that may contain various DDoS attacks or flash crowds. Intense pulse in the traffic means possible existence of anomalies because this is the basic property of DDoS attacks and flash crowds. Once abnormal traffic is identified, the sensor sends ATTENTION signal to activate the attack detection module. Second, CALD dynamically records the average frequency of each source IP and check the total mess extent. Theoretically, the mess extent of DDoS attacks is larger than the one of flash crowds. Thus, with some parameters from the attack detection module, the filter is capable of letting the legitimate requests through but the attack traffic stopped. Third, CALD may divide the security modules away from the Web servers. As a result, it keeps maximum performance on the kernel web services, regardless of the harassment from DDoS. In the experiments, the records from www.sina.com and www.taobao.com have proved the value of CALD
GRIM: GRaph-based Interactive narrative visualization for gaMes
Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The
narratives of these may take years to write and typically involve a large
creative team. In this work, we demonstrate the potential of large generative
text models to assist this process. \textbf{GRIM}, a prototype
\textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for
ga\textbf{M}es, generates a rich narrative graph with branching storylines that
match a high-level narrative description and constraints provided by the
designer. Game designers can interactively edit the graph by automatically
generating new sub-graphs that fit the edits within the original narrative and
constraints. We illustrate the use of \textbf{GRIM} in conjunction with GPT-4,
generating branching narratives for four well-known stories with different
contextual constraints
Novel hybrids of natural β-elemene bearing isopropanolamine moieties: synthesis, enhanced anticancer profile, and improved aqueous solubility
A series of novel β-elemene isopropanolamine derivatives were synthesized and evaluated for their antitumor activity. The results indicated that all of the compounds showed stronger antiproliferative activities than β-elemene as well as improved aqueous solubility. In particular dimer 6q showed the strongest cytotoxicity against four tumor cell lines (SGC-7901, HeLa, U87 and A549) with IC50 values ranging from 4.37 to 10.20 μM. Moreover, combination of 6q with cisplatin exhibited a synergistic effect on these cell lines with IC50 values ranging from 1.21 to 2.94 μM, and reversed the resistance of A549/DPP cells with an IC50 value of 2.52 μM. The mechanism study revealed that 6q caused cell cycle arrest at the G2 phase and induced apoptosis of SGC-7901 cells through a mitochondrial-dependent apoptotic pathway. Further in vivo study in H22 liver cancer xenograft mouse model validated the antitumor activity of 6q with a tumor inhibitory ratio (TIR) of 60.3%, which was higher than that of β-elemene (TIR, 49.1%) at a dose of 60 mg/kg. Altogether, the potent antitumor activity of 6qin vitro and in vivo warranted further preclinical investigation for potential anticancer chemotherapy
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